Optimal auction method for UAV edge computing resources based on algorithm mechanism design

Abstract

The present invention provides an optimal auction method for edge computing resources of unmanned aerial vehicles based on algorithm mechanism design, which belongs to the field of unmanned aerial vehicle technology. The method includes: collecting mission scenario characterization information; constructing a computing resource auction social welfare maximization model based on the mission scenario characterization information; solving the computing resource auction social welfare maximization model using a genetic algorithm to obtain an optimal flight plan; and determining an optimal payment and allocation mechanism based on the optimal flight plan. The method studies the social welfare maximization problem by characterizing a scenario in which a drone is used to perform auxiliary edge computing on multiple mission points, and the revenue valuation of the computing resources by the mission points is private information and the computing amount is public knowledge. It can determine the optimal payment and allocation mechanism for the edge computing resources of the drone, and can provide guidance for improving the overall performance of the drone-assisted edge computing system under multi-agents.

Description

Technical Field

[0001] The present invention belongs to the technical field of unmanned aerial vehicles, and more specifically, relates to an optimal auction method for edge computing resources of unmanned aerial vehicles based on algorithm mechanism design. Background Art

[0002] With the explosive growth of high mobility and data traffic, drone-assisted wireless communication has attracted widespread attention. Compared with traditional wireless communication, drone-assisted wireless communication can provide higher wireless connectivity in areas without infrastructure coverage. In addition, drone-assisted wireless communication can always achieve higher throughput due to the high probability of line-of-sight communication link between user equipment and drones. Therefore, drones can be used for various applications such as drone relay, drone data collection, drone device-to-device communication network, drone wireless power transmission network, and drone caching network.

[0003] Due to the mobility of drones, integrating drone-supported communications with mobile edge computing can further improve computing performance. In the scenario of drone-assisted edge computing resources, the energy consumption of a single drone flight is limited, and the degree of demand for computing resources at different mission points is usually its own private information. In the above scenario, it is necessary to study the computing resource allocation and payment mechanism that satisfies the incentive compatibility of dominant strategies and non-negative utility constraints to maximize social welfare; however, there is currently a lack of research on the use of drones to assist edge computing scenarios for multiple mission points, and a lack of optimal auction methods for drone edge computing resources. Summary of the invention

[0004] The main purpose of this invention is to provide an optimal auction method for drone edge computing resources based on algorithm mechanism design, which can provide guidance for improving the overall performance of drone-assisted edge computing systems under multi-agents to maximize social welfare.

[0005] In order to achieve the above objectives, the present invention proposes an optimal auction method for UAV edge computing resources based on algorithm mechanism design, including:

[0006] Collect mission scenario representation information;

[0007] Based on the task scenario representation information, a computing resource auction social welfare maximization model is constructed;

[0008] Using genetic algorithm to solve the computing resource auction social welfare maximization model to obtain the optimal flight plan;

[0009] Based on the optimal flight plan, an optimal payment and allocation mechanism is determined.

[0010] Furthermore, the social welfare maximization model of computing resource auction is constructed based on the task scenario characterization information, including:

[0011] With the constraints of satisfying the incentive compatibility and non-negative utility of dominant strategies, the goal of maximizing social welfare, and the allocation mechanism and payment mechanism in computing resource auctions as decision variables, a computing resource auction social welfare maximization model is constructed:

[0012]

[0013] In the formula, Represents social welfare, represents the revenue estimate of task point n, represents the mathematical expectation operation, It indicates the amount of computing resources allocated to task point n when it bids according to its revenue valuation.

[0014] Furthermore, the method of solving the computing resource auction social welfare maximization model using a genetic algorithm to obtain an optimal flight plan includes the following steps:

[0015] S301, genetic algorithm uses 0-1 encoding, the length of each chromosome is ; In any flight plan, if the UAV passes through the line between task point i and task point j, the corresponding position on the chromosome is 1, otherwise the corresponding position on the chromosome is 0;

[0016] S302, randomly generate Q flight plan codes as an initial population;

[0017] S303. For each chromosome, if the corresponding flight plan is not feasible or the flight energy consumption exceeds the upper limit of the energy consumption of a single flight of the UAV, its fitness is set to 0; otherwise, its fitness is set to the sum of the bids of all the task points along the way;

[0018] S304, using the index sorting selection method to select individuals in the current population for replication;

[0019] S305, randomly pairing the individuals generated by the selection-copy operation;

[0020] S306, based on mutation probability Perform mutation operations;

[0021] S307, wherein, step S304 to step S306 is one iteration, and the iteration is repeated until it reaches generation Z, and the total bid sum B and the code with the highest fitness at this time are output, and the optimal flight plan is obtained after decoding.

[0022] Furthermore, determining the optimal payment and allocation mechanism based on the optimal flight plan includes the following steps:

[0023] S401. In the optimal flight plan, if the UAV passes through task point n, the allocation decision for task point n is recorded as , otherwise the allocation decision for task point n is recorded as ;

[0024] S402: For any route mission point n in the optimal flight plan, calculate the sum of the bids of all other route mission points except it. ;

[0025] S403: For any task point n in the optimal flight plan, remove the point from all task points and recalculate using the genetic algorithm, and output the sum of the bids at this time. ;

[0026] S404: In the optimal flight plan, if the drone passes through mission point n, the payment for mission point n is recorded as , otherwise the payment for task point n is recorded as ;

[0027] S405. Combining step S401 and step S404, an optimal payment and allocation mechanism is obtained.

[0028] Furthermore, the collected task scenario characterization information includes: characterizing a scenario in which a drone is used to assist edge computing for multiple task points, and the task points' revenue valuation of computing resources is private information while the computing amount is public knowledge.

[0029] Furthermore, the mission scenario characterization information includes: energy consumption of the drone, flight speed of the drone, calculation speed of the drone, calculation amount of the mission point, and revenue estimation of the mission point.

[0030] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of any one of the above-described methods when executing the computer program.

[0031] The present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores machine-executable instructions. When the machine-executable instructions are called and executed by a processor, the machine-executable instructions prompt the processor to execute any of the above-mentioned methods.

[0032] Compared with the prior art, the present invention has the following beneficial effects: by characterizing the scenario in which drones are used to assist edge computing for multiple mission points, and the revenue valuation of computing resources by the mission points is private information while the computing amount is public knowledge, the problem of social welfare maximization is studied, which can provide guidance for the overall performance improvement of drone-assisted edge computing systems under multi-agents; and a method for optimal auction of computing resources based on genetic algorithms for social welfare maximization that satisfies the incentive compatibility constraints of dominant strategies and the non-negative utility constraints is provided, which can determine the optimal payment and allocation mechanism of drone edge computing resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 A schematic diagram of a flow chart of an optimal auction method for edge computing resources of a drone based on an algorithm mechanism design provided by an embodiment of the present invention;

[0034] Figure 2 A schematic diagram of a scenario of an embodiment provided for an embodiment of the present invention;

[0035] Figure 3 It is a schematic diagram of a task scenario of an embodiment provided by an embodiment of the present invention;

[0036] Figure 4 It is a schematic diagram of an optimal allocation mechanism and payment mechanism provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0037] The embodiments of the present disclosure are described in detail below with reference to the accompanying drawings.

[0038] The following describes the embodiments of the present disclosure through specific examples, and those skilled in the art can easily understand other advantages and effects of the present disclosure from the contents disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of the present disclosure, rather than all of the embodiments. The present disclosure can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present disclosure. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of the present disclosure.

[0039] Figure 1 A flowchart of an optimal auction method for edge computing resources of a drone based on an algorithm mechanism design provided by an embodiment of the present invention is provided. Figure 2 The following is a schematic diagram of an embodiment of the method of the present invention, wherein the method comprises the following steps:

[0040] S100: Collect task scenario representation information.

[0041] Among them, the scenario where drones are used to assist edge computing for multiple mission points, and the mission points’ valuation of computing resources’ benefits is private information while the amount of computing is public knowledge is characterized. The characterization includes the following processes:

[0042] The task points are numbered to form a task point number set:

[0043] (14)

[0044] In formula (14), represents a set of numbers of N task points; in this embodiment, , the mission point is Figure 3 The circle in the figure indicates that the task point number is marked in the circle;

[0045] The take-off / return point of the drone is numbered as 0; in this embodiment, the take-off / return point of the drone is Figure 3 The triangle in the figure indicates the number of the take-off / return point of the drone.

[0046] Define the task point calculation set:

[0047] (15)

[0048] In formula (15), represents the set of computational loads of N task points; where, represents the computational effort of task point n, is public knowledge; in this embodiment, the calculation amount of all task points is 20;

[0049] The calculation speed of the drone is ; In this embodiment, the calculation speed of the drone is 10;

[0050] Define the revenue valuation set of the task point:

[0051] (16)

[0052] In formula (16), represents the set of revenue estimates of N task points, represents the revenue estimate of task point n, is the private information of task point n; in this embodiment, the estimated revenue of each task point is Figure 3 The number corresponding to the circle above;

[0053] Define the distance between any two points:

[0054] (17)

[0055] In formula (17), represents the distance between the point numbered i and the point numbered j. and Respectively represent the two-dimensional coordinates of the point numbered i and the point numbered j;

[0056] The flight speed of the drone is ,In this embodiment, the flight speed of the drone is 120;

[0057] The energy consumption per unit time of UAV flight is , in this embodiment, ;

[0058] The hovering energy consumption per unit time of the drone is , in this embodiment, ;

[0059] The upper limit of energy consumption of a single flight of a drone is , in this embodiment, .

[0060] S200: Based on the task scenario characterization information, a computing resource auction social welfare maximization model is constructed.

[0061] Among them, the social welfare maximization model of computing resource auction is constructed with the constraints of satisfying the incentive compatibility and non-negative utility of dominant strategies and the goal of maximizing social welfare, and the allocation mechanism and payment mechanism in computing resource auction as decision variables. Specifically, it includes the following processes:

[0062] The computing resource quotation vector of the task point:

[0063] (18)

[0064] In formula (18), The computational resource quotation vector for all task points, Quote the computing resources for task point n;

[0065] The function vector of the allocation mechanism of drones:

[0066] (19)

[0067] In formula (19), is the UAV allocation mechanism function vector, is the allocation decision for task point n; where, is a random variable;

[0068] The payment mechanism function vector of the drone:

[0069] (20)

[0070] In formula (20), is the payment mechanism function vector of the drone, is the payment for task point n;

[0071] Constrain the value of the allocation mechanism function:

[0072] (twenty one);

[0073] Constrain the value of the payment mechanism function:

[0074] (twenty two);

[0075] The dominant strategy incentive compatibility constraint of task point n;

[0076] (twenty three),

[0077] Formula (23) shows that truthfully reporting one's own computing resource benefit valuation is the dominant strategy for task point n. where represents the quotation vector of all task points except task point n; It represents the mathematical expectation operation;

[0078] The non-negative utility constraint in the incentive compatibility of the dominant strategy of task point n;

[0079] (twenty four),

[0080] Formula (24) shows that the expected utility of a task point n that truthfully reports its own computing resource benefit valuation is non-negative;

[0081] Energy consumption constraints of a single UAV flight;

[0082] (25),

[0083] In formula (25), represents an algorithm for calculating the optimal flight path of a UAV. The input of the algorithm is the quotation vector of the mission point, and the output is the energy consumption under the optimal flight path; The energy consumption limit of a single flight of the drone;

[0084] The objective function is set to maximize social welfare:

[0085] (26),

[0086] In formula (26), Represents social welfare.

[0087] Among them, formula (26) can be simply expressed as a computing resource auction social welfare maximization model. By summarizing formulas (18) to (26), we can obtain the following specific computing resource auction social welfare maximization model:

[0088] (27).

[0089] S300, using a genetic algorithm to solve the computing resource auction social welfare maximization model to obtain an optimal flight plan. Specifically, the process includes the following:

[0090] S301, genetic algorithm uses 0-1 encoding, the length of each chromosome is ; In any flight plan, if the UAV passes through the line between task point i and task point j, the corresponding position of the chromosome is 1, otherwise the corresponding position of the chromosome is 0; in this embodiment, the length of the chromosome is 66;

[0091] S302, randomly generate Q flight plan codes as the initial population; in this embodiment, ;

[0092] S303. For each chromosome, if the corresponding flight plan is not feasible or the flight energy consumption exceeds the upper limit of the energy consumption of a single flight of the UAV, its fitness is set to 0; otherwise, its fitness is set to the sum of the bids of all the task points along the way;

[0093] S304, using the index sorting selection method to select individuals in the current population for replication;

[0094] S305, randomly pairing the individuals generated by the selection-copy operation;

[0095] S306, based on mutation probability Perform a mutation operation; in this embodiment, ;

[0096] S307, wherein step S304 to step S306 is one iteration, and the iteration is repeated until it reaches generation Z, and the sum of the bids B and the code with the maximum fitness at this time are output, and the optimal flight plan is obtained after decoding; wherein, in this embodiment, .

[0097] S400: Determine the optimal payment and allocation mechanism based on the optimal flight plan. Specifically, the process includes the following steps:

[0098] S401. In the optimal flight plan, if the UAV passes through task point n, the allocation decision for task point n is recorded as , otherwise the allocation decision for task point n is recorded as ;

[0099] S402: For any route mission point n in the optimal flight plan, calculate the sum of the bids of all other route mission points except it. ;

[0100] S403: For any task point n in the optimal flight plan, remove the point from all task points and recalculate using the genetic algorithm, and output the sum of the bids at this time. ;

[0101] S404: In the optimal flight plan, if the drone passes through mission point n, the payment for mission point n is recorded as , otherwise the payment for task point n is recorded as ;

[0102] S405. Combining step S401 and step S404, an optimal payment and allocation mechanism is obtained. In this embodiment, the optimal payment and allocation mechanism is: Figure 4 Indicates; the arrow direction indicates the flight direction of the drone, the allocation amount of all the mission points along the way is 1, and the rest are 0, and the rectangle next to the mission point along the way indicates the payment amount of the mission point.

[0103] This embodiment also provides an electronic device, including: a processor, a memory and a bus, the memory storing machine-readable instructions executable by the processor, when the electronic device is running, the processor and the memory communicate through the bus, and the processor executes the machine-readable instructions to perform the steps of the optimal auction method for drone edge computing resources designed based on the algorithm mechanism as described above.

[0104] Among them, the above-mentioned memory and processor can be general-purpose memory and processor, which are not specifically limited here. When the processor runs the computer program stored in the memory, it can execute the above-mentioned optimal auction method for edge computing resources of unmanned aerial vehicles designed based on the algorithm mechanism. The processor may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above-mentioned method can be completed by an integrated logic circuit of hardware in the processor or an instruction in the form of software. The above-mentioned processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The disclosed methods, steps and logic block diagrams in the embodiments of the present invention can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor. The steps of the method disclosed in the embodiment of the present invention can be directly embodied as being executed by a hardware decoding processor, or can be executed by a combination of hardware and software modules in the decoding processor. The software module can be located in a mature storage medium in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. The storage medium is located in a memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware.

[0105] An embodiment of the present invention also provides a computer-readable storage medium, which corresponds to the above-mentioned optimal auction method for drone edge computing resources based on algorithm mechanism design. The computer-readable storage medium stores machine-executable instructions. When the computer-executable instructions are called and executed by a processor, the computer-executable instructions prompt the processor to execute the steps of the above-mentioned optimal auction method for drone edge computing resources based on algorithm mechanism design.

[0106] In the description of the present invention, it is necessary to understand that the terms "middle", "length", "up", "down", "front", "back", "vertical", "horizontal", "inside", "outside", "radial", "circumferential" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention.

[0107] In the present invention, unless otherwise clearly specified and limited, the first feature "on" the second feature may be that the first and second features are in direct contact, or the first and second features are in indirect contact through an intermediate medium. "Multiple" means at least two, such as two, three, etc., unless otherwise clearly and specifically limited.

[0108] In the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral one; it can be a mechanical connection, an electrical connection, or communication with each other; it can be a direct connection, or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements, unless otherwise clearly defined. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0109] The above is only for explaining the implementation mode of the present invention and is not intended to limit the present invention. For those skilled in the art, any modification, equivalent substitution, improvement, etc. made within the spirit and principle of the present invention without creative work should be included in the protection scope of the present invention.

Claims

1. An optimal auction method for UAV edge computing resources based on algorithm mechanism design, characterized in that: include: Collect mission scenario representation information; Based on the task scenario characterization information, a computing resource auction social welfare maximization model is constructed; wherein, the social welfare maximization model is constructed with the constraints of satisfying the incentive compatibility and non-negative utility of the dominant strategy, the goal of maximizing social welfare, and the allocation mechanism and payment mechanism in the computing resource auction as decision variables: In the formula, G represents social welfare, v n represents the estimated benefit of task point n, E(·) represents the mathematical expectation operation, X n (v,ξ) represents the amount of computing resources allocated to task point n when it bids according to its revenue valuation. v represents the revenue valuation vector of N task points, and ξ is a random variable. The method uses a genetic algorithm to solve the computing resource auction social welfare maximization model to obtain the optimal flight plan; the method includes the following steps: S301, genetic algorithm uses 0-1 encoding, the length of each chromosome is Where N represents the number of mission points. In any flight plan, if the UAV passes through the line between mission point i and mission point j, the corresponding position on the chromosome is 1, otherwise the corresponding position on the chromosome is 0. S302, randomly generate Q flight plan codes as an initial population; S303. For each chromosome, if the corresponding flight plan is not feasible or the flight energy consumption exceeds the upper limit of the energy consumption of a single flight of the UAV, its fitness is set to 0; otherwise, its fitness is set to the sum of the bids of all the task points along the way; S304, using the index sorting selection method to select individuals in the current population for replication; S305, randomly pairing the individuals generated by the selection-copy operation; S306, performing a mutation operation with a mutation probability γ; S307, wherein step S304 to step S306 is one iteration, and the iteration is repeated until it reaches generation Z, and the total bid amount B and the code with the maximum fitness at this time are output, and the optimal flight plan is obtained after decoding; Based on the optimal flight plan, determining the optimal payment and allocation mechanism; comprising the following steps: S401: In the optimal flight plan, if the UAV passes through task point n, the allocation decision for task point n is recorded as X. n (b,ξ)=1, otherwise the allocation decision of task point n is recorded as X n (b,ξ)=0; where X n (b,ξ) is the allocation decision for task point n, ξ is a random variable, and b is the computing resource quotation vector of all task points; S402: For any route mission point n in the optimal flight plan, calculate the sum of the bids B of all other route mission points except it. -n ; S403: For any task point n in the optimal flight plan, remove the point from all task points and recalculate using the genetic algorithm, and output the total bid at this time. S404: In the optimal flight plan, if the drone passes through mission point n, the payment for mission point n is recorded as Otherwise, the payment for task point n is recorded as p n (b,ξ)=0; where p n (b,ξ) is the payment for task point n, ξ is a random variable, b is the computing resource quotation vector of all task points, The sum of the quotations at this time, B -n It is the sum of the bids of all other path mission points except this one; S405. Combining step S401 and step S404, an optimal payment and allocation mechanism is obtained.

2. The optimal auction method for UAV edge computing resources based on algorithm mechanism design according to claim 1 is characterized in that: The collected task scenario characterization information includes: characterizing a scenario in which a drone is used to assist edge computing for multiple task points, and the task points' revenue valuation of computing resources is private information while the computing amount is public knowledge.

3. The optimal auction method for UAV edge computing resources based on algorithm mechanism design according to claim 2 is characterized in that: The mission scenario characterization information includes: the energy consumption of the drone, the flight speed of the drone, the calculation speed of the drone, the calculation amount of the mission point, and the revenue estimation of the mission point.

4. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 3 are implemented.

5. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores machine-executable instructions. When the machine-executable instructions are called and executed by a processor, the machine-executable instructions cause the processor to execute the method according to any one of claims 1 to 3.

Images